Abstract: This research paper introduces an innovative approach to revaluation panel using dimension analysis, aiming to improve the accuracy and efficiency of data prediction. By harnessing a range of mathematical tools, this method enables the identification and comprehension of diverse dimensions within a dataset. The extracted information is then employed to construct a predictive model, facilitating more reliable value predictions. Compared to conventional techniques, the proposed approach offers several advantages. Firstly, it enhances accuracy by considering the multifaceted nature of the dataset. Secondly, it proves to be highly efficient, especially when dealing with large datasets. Lastly, its transparency empowers the panel to comprehend the model's development process, promoting greater understanding and trust in the results. The effectiveness of the proposed approach is demonstrated through its application to a real-world dataset. The obtained results convincingly establish its superiority in accuracy and efficiency over traditional methods.

Keywords: Dimension analysis, data prediction, predictive model, real-world data, large dataset

| DOI: 10.17148/IARJSET.2023.10769